Methods and devices using meta-features extracted from accelerometry signals for swallowing impairment detection

ABSTRACT

A method can classify cervical accelerometry data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method can include receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from an accelerometer operatively coupled to a processing module that is a local or remote computing device. The method can include extracting one or more specific meta-features from the data and then outputting from the processing module a classification of the swallowing event based on the extracted meta-features, for example a first classification indicative of normal swallowing or a second classification indicative of possibly impaired swallowing.

BACKGROUND

The present disclosure generally relates to methods and devices forusing meta-features extracted from accelerometry signals for swallowingimpairment detection, whereby a candidate executes one or moreswallowing events and dual axis accelerometry data is acquiredrepresentative thereof. More specifically, the present disclosurerelates to specific meta-features extracted from axis-specificaccelerometry signals.

Dysphagia is characterized by impaired involuntary motor control ofswallowing process and can cause “penetration” which is the entry offoreign material into the airway. The airway invasion can be accompaniedby “aspiration” in which the foreign material enters the lungs and canlead to serious health risks.

The three phases of swallowing activity are oral, pharyngeal andesophageal. The pharyngeal phase is typically compromised in patientswith dysphagia. The impaired pharyngeal phase of swallowing in dysphagiais a prevalent health condition (38% of the population above 65 years)and may result in prandial aspiration (entry of food into the airway)and/or pharyngeal residues, which in turn can pose serious health riskssuch as aspiration pneumonia, malnutrition, dehydration, and even death.Swallowing aspiration can be silent (i.e., without any overt signs ofswallowing difficulty such as cough), especially in children withdysphagia and patients with acute stroke, rendering detection viaclinical perceptual judgement difficult.

The current gold standard for tracking swallowing activities isvideofluoroscopy that enables clinicians to monitor barium-infusedfoodstuff during swallowing via moving x-ray images. However, thevideofluoroscopy swallowing study (VFSS) cannot be done routinely due tothe expensive procedure and the need for specialized personnel, as wellas the excessive amount of harmful radiations. Another invasiveassessment is the flexible endoscopic evaluation of swallowing, whichalso requires trained personnel and entails an expensive procedure.Non-invasive alternatives for swallow monitoring include surfaceelectromyography, pulse oximetry, cervical auscultation (listening tothe breath sounds near the larynx) and swallowing accelerometry.

Despite introduction of different non-invasive approaches, a reliablebedside detection of swallowing abnormalities remains a challengingtask. For example, a recent systematic review of cervical auscultationstudies suggests that the reliability of the approach is insufficientand it can not be used as a stand-alone instrument to diagnosedysphagia. Lagarde, Marloes L J and Kamalski, Digna M A and van denEngel-Hoek, Lenie, “The reliability and validity of cervicalauscultation in the diagnosis of dysphagia: A systematic review,”Clinical Rehabilitation 30(2):1-9 (March 2015). Furthermore, perceptualclinical screening of dysphagia has been shown to lack agreement betweendifferent speech-language pathologists, possibly due to the subjectivenature of the judgement as well as the presence of variety ofenvironmental artifacts.

Over the past two decades, researchers have reported on variousswallowing screening tools, among which those driven by swallowingsounds are the most popular. Swallowing sounds are either capturedacoustically using a microphone or mechanically using an accelerometerplaced on the patient's neck measuring cervical epidermal vibrations.Reports on discriminative analysis of swallowing auscultation signalsvary in terms of the screening tool used, target swallowing problem(aspiration, penetration, pharyngeal residue), sample size, patientpopulation and medical conditions, and validation approach, which makesa direct comparison between these studies virtually impossible.

Swallowing accelerometry harnesses the hyoid and laryngeal movementsduring swallowing activities, which are manifested as epidermalvibrations measurable at the neck by an accelerometer. Vibrations inboth the anterior-posterior (A-P) and superior-inferior (S-I) anatomicaldirections are found to contain distinct information about theunderlying swallowing activities.

Nevertheless, the development of a fully automated, accurate swallowingscreening tool remains an elusive challenge.

SUMMARY

In a general embodiment, the present disclosure provides a device foridentifying a possible swallowing impairment in a candidate duringexecution of a swallowing event. The device comprises: an accelerometerconfigured to acquire axis-specific vibrational data along ananterior-posterior (A-P) axis and a superior-inferior (S-I) axis of thecandidate's throat, the axis-specific vibrational data is representativeof the swallowing event; and a processing module that is a local orremote computing device operatively coupled to the accelerometer, theprocessing module configured for processing the axis-specific data toextract meta-features from the data, one or more of the meta-featuresassociated with an approach selected from the group consisting of (i)swallow segmentation using spectrogram, (ii) sound direction for anon-segmented spectrogram, (iii) difference between SI and AP signalsregarding correlation coefficient between residual and basic signals fora non-segmented spectrogram, (iv) difference between SI and AP signalsregarding residual peaking feature for a non-segmented spectrogram, (v)velocity and position for integrating velocity and position from sensorsignals, (vi) basic signal statistics for segmented spectrogram, (vii)spectral entropy at different bandwidths for segmented spectrogram,(viii) direction of focus of spectrogram components for segmentedspectrogram, (ix) spectral entropy for the spectrogram taken as adifference between low-frequencies and high-frequencies for segmentedspectrogram, (x) PCA from spectrogram, measuring percentage of varianceexplained by 1st or 2nd PCA component, either in time or frequency axisfor segmented spectrogram, (xi) texture features from spectrogram imagefor specture, and (xii) signal entropy for head and swallow signals. Theprocessing module is configured to classify the swallowing event as oneof a plurality of classifications based on the meta-features extractedfrom the vibrational data, the plurality of classifications comprising afirst classification indicative of normal swallowing and a secondclassification indicative of possibly impaired swallowing.

In an embodiment, the processing module is configured to automaticallyextract the meta-features from the data. The processing module can beconfigured to automatically use the meta-features extracted from thedata to classify the swallowing event.

In an embodiment, the processing module is configured to compare themeta-features extracted from the data to preset classification criteriato classify the swallowing event. The preset classification criteria canbe defined for each of swallowing safety and swallowing efficiencyand/or defined by features previously extracted and classified from aknown training data set.

In an embodiment, the second classification is indicative of at leastone of a swallowing safety impairment or a swallowing efficiencyimpairment.

In an embodiment, the second classification is indicative of at leastone of penetration or aspiration, and the processing module isconfigured to further classify the swallowing event as a first eventindicative of a safe event or a second event indicative of an unsafeevent.

In an embodiment, the processing module is configured to classifymultiple successive swallowing events by classifying the data for eachof the successive swallowing events as indicative of one of the firstclassification or the second classification.

In an embodiment, the processing module displays the classification.

In another general embodiment, the present disclosure provides a methodfor classifying cervical accelerometry data acquired for a swallowingevent to identify a possible swallowing impairment in a candidate. Themethod comprises: receiving axis-specific vibrational data for ananterior-posterior (A-P) axis and a superior-inferior (S-I) axis andrepresentative of the swallowing event, a processing module that is alocal or remote computing device operatively coupled to an accelerometerreceives the axis-specific vibrational data from the accelerometer;processing the axis-specific data to extract meta-features from thedata, one or more of the meta-features associated with an approachselected from the group consisting of (i) swallow segmentation usingspectrogram, (ii) sound direction for a non-segmented spectrogram, (iii)difference between SI and AP signals regarding correlation coefficientbetween residual and basic signals for a non-segmented spectrogram, (iv)difference between SI and AP signals regarding residual peaking featurefor a non-segmented spectrogram, (v) velocity and position forintegrating velocity and position from sensor signals, (vi) basic signalstatistics for segmented spectrogram, (vii) spectral entropy atdifferent bandwidths for segmented spectrogram, (viii) direction offocus of spectrogram components for segmented spectrogram, (ix) spectralentropy for the spectrogram taken as a difference betweenlow-frequencies and high-frequencies for segmented spectrogram, (x) PCAfrom spectrogram, measuring percentage of variance explained by 1st or2nd PCA component, either in time or frequency axis for segmentedspectrogram, (xi) texture features from spectrogram image for specture,and (xii) signal entropy for head and swallow signals; and outputting aclassification of the swallowing event as one of a plurality ofclassifications based on the meta-features extracted from the data, theplurality of classifications comprising a first classificationindicative of normal swallowing and a second classification indicativeof possibly impaired swallowing, and the processing module outputs theclassification.

In an embodiment, the processing module automatically extracts themeta-features from the data. The processing module can automatically usethe meta-features extracted from the data to classify the swallowingevent on the processing module.

In an embodiment, the method comprises comparing the meta-featuresextracted from the data to preset classification criteria to classifythe swallowing event on the processing module. The preset classificationcriteria can be defined for each of swallowing safety and swallowingefficiency and/or defined by features previously extracted andclassified from a known training data set.

In an embodiment, the second classification is indicative of at leastone of a swallowing safety impairment or a swallowing efficiencyimpairment.

In an embodiment, the second classification is indicative of at leastone of penetration or aspiration, and the method comprises furtherclassifying the swallowing event as a first event indicative of a safeevent or a second event indicative of an unsafe event.

In an embodiment, the method comprises classifying successive swallowingevents by classifying the data for each of the successive swallowingevents as indicative of one of the first classification or the secondclassification.

In an embodiment, the method comprises displaying the classification onthe processing device.

In yet another general embodiment, the present disclosure provides amethod of treating dysphagia in a patient. The method comprises:positioning a sensor externally on the throat of the patient, the sensoracquiring vibrational data representing swallowing activity andassociated with an anterior-posterior axis and a superior-inferior axisof the throat, the sensor operatively connected to a processing moduleconfigured to process the axis-specific data to extract meta-featuresfrom the data, one or more of the meta-features associated with anapproach selected from the group consisting of (i) swallow segmentationusing spectrogram, (ii) sound direction for a non-segmented spectrogram,(iii) difference between SI and AP signals regarding correlationcoefficient between residual and basic signals for a non-segmentedspectrogram, (iv) difference between SI and AP signals regardingresidual peaking feature for a non-segmented spectrogram, (v) velocityand position for integrating velocity and position from sensor signals,(vi) basic signal statistics for segmented spectrogram, (vii) spectralentropy at different bandwidths for segmented spectrogram, (viii)direction of focus of spectrogram components for segmented spectrogram,(ix) spectral entropy for the spectrogram taken as a difference betweenlow-frequencies and high-frequencies for segmented spectrogram, (x) PCAfrom spectrogram, measuring percentage of variance explained by 1st or2nd PCA component, either in time or frequency axis for segmentedspectrogram, (xi) texture features from spectrogram image for specture,and (xii) signal entropy for head and swallow signals, the processingmodule configured to classify the swallowing event as one of a pluralityof classifications based on the meta-features extracted from thevibrational data, the plurality of classifications comprising a firstclassification indicative of normal swallowing and a secondclassification indicative of possibly impaired swallowing; and adjustinga feeding administered to the patient based on the classification.

In an embodiment, the adjusting of the feeding is selected from thegroup consisting of: changing a consistency of the feeding, changing atype of food in the feeding, changing a size of a portion of the feedingadministered to the patient, changing a frequency at which portions ofthe feeding are administered to the patient, and combinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is diagram showing the axes of acceleration in theanterior-posterior and superior-inferior directions.

FIG. 2 is a schematic diagram of an embodiment of a swallowingimpairment detection device in operation.

FIG. 3 is a schematic diagram of an embodiment of a method ofdiscriminating swallowing aspiration-penetration.

DETAILED DESCRIPTION

As used in this disclosure and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. As used herein, “about” is understood to refer tonumbers in a range of numerals, for example the range of −10% to +10% ofthe referenced number, preferably −5% to +5% of the referenced number,more preferably −1% to +1% of the referenced number, most preferably−0.1% to +0.1% of the referenced number. Moreover, all numerical rangesherein should be understood to include all integers, whole or fractions,within the range.

The words “comprise,” “comprises” and “comprising” are to be interpretedinclusively rather than exclusively. Likewise, the terms “include,”“including” and “or” should all be construed to be inclusive, unlesssuch a construction is clearly prohibited from the context. A disclosureof a device “comprising” several components does not require that thecomponents are physically attached to each other in all embodiments.

Nevertheless, the devices disclosed herein may lack any element that isnot specifically disclosed. Thus, a disclosure of an embodiment usingthe term “comprising” includes a disclosure of embodiments “consistingessentially of” and “consisting of” the components identified.Similarly, the methods disclosed herein may lack any step that is notspecifically disclosed herein. Thus, a disclosure of an embodiment usingthe term “comprising” includes a disclosure of embodiments “consistingessentially of” and “consisting of” the steps identified.

The term “and/or” used in the context of “X and/or Y” should beinterpreted as “X,” or “Y,” or “X and Y.” Where used herein, the terms“example” and “such as,” particularly when followed by a listing ofterms, are merely exemplary and illustrative and should not be deemed tobe exclusive or comprehensive. Any embodiment disclosed herein can becombined with any other embodiment disclosed herein unless explicitlystated otherwise.

As used herein, a “bolus” is a single sip or mouthful or a food orbeverage. As used herein, “aspiration” is entry of food or drink intothe trachea (windpipe) and lungs and can occur during swallowing and/orafter swallowing (post-deglutitive aspiration). Post-deglutitiveaspiration generally occurs as a result of pharyngeal residue thatremains in the pharynx after swallowing.

An aspect of the present disclosure is a method of processing dual-axisaccelerometry signals to classify one or more swallowing events. Anon-limiting example of such a method classifies each of the one or moreswallowing events as a swallow with aspiration-penetration or a swallowwithout aspiration-penetration. Another aspect of the present disclosureis a device that implements one or more steps of the method.

In an embodiment, the method can further comprise classifying thepatient as having safe swallowing or unsafe swallowing. For example, apatient can be classified as having unsafe swallowing if the one or moreswallowing events comprise an amount or percentage ofaspiration-penetration events that exceeds a threshold. In such anembodiment, the threshold can be zero such that the presence of anyaspiration-penetration events classifies the patient as having unsafeswallowing. Of course, in other such embodiments, the threshold can begreater than zero.

In some embodiments, the method and the device can be employed in theapparatus and/or the method for detecting aspiration disclosed in U.S.Pat. No. 7,749,177 to Chau et al., the method and/or the system ofsegmentation and time duration analysis of dual-axis swallowingaccelerometry signals disclosed in U.S. Pat. No. 8,267,875 to Chau etal., the system and/or the method for detecting swallowing activitydisclosed in U.S. Pat. No. 9,138,171 to Chau et al., or the methodand/or the device for swallowing impairment detection disclosed in U.S.Patent App. Publ. No. 2014/0228714 to Chau et al., each of which isincorporated herein by reference in its entirety.

As discussed in greater detail hereafter, the device may include asensor configured to produce signals indicating swallowing activities(e.g., a dual axis accelerometer). The sensor may be positionedexternally on the neck of a human, preferably anterior to the cricoidcartilage of the neck. A variety of means may be applied to position thesensor and to hold the sensor in such position, for example double-sidedtape. Preferably the positioning of the sensor is such that the axes ofacceleration are aligned to the anterior-posterior and super-inferiordirections, as shown in FIG. 1. As used herein, the anterior-posterior(A-P) axis and the superior-inferior (S-I) axis are relative to thecandidate's throat.

FIG. 2 generally illustrates a non-limiting example of a device 100 foruse in swallowing impairment detection. The device 100 can comprise asensor 102 (e.g., a dual axis accelerometer) to be attached in a throatarea of a candidate for acquiring dual axis accelerometry data and/orsignals during swallowing, for example illustrative S-I accelerationsignal 104. Accelerometry data may include, but is not limited to,throat vibration signals acquired along the anterior-posterior axis(A-P) and/or the superior-inferior axis (S-I). The sensor 102 can be anyaccelerometer known to one of skill in this art, for example a singleaxis accelerometer (which can be rotated on the patient to obtaindual-axis vibrational data) such as an EMT 25-C single axisaccelerometer or a dual axis accelerometer such as an ADXL322 or ADXL327dual axis accelerometer, and the present disclosure is not limited to aspecific embodiment of the sensor 102.

The sensor 102 does not necessarily need to be fixed exactly in A-P andS-I orientation. In this regard, the sensor 102 can merely measure intwo perpendicular directions along the sagittal plane of the subject,and the acceleration vectors in both S-I and A-P directions can beextracted from the two sensor signals.

The sensor 102 can be operatively coupled to a processing module 106configured to process the acquired data for swallowing impairmentdetection, for example aspiration-penetration detection and/or detectionof other swallowing impairments such as swallowing inefficiencies. Theprocessing module 106 can be a distinctly implemented device operativelycoupled to the sensor 102 for communication of data thereto, forexample, by one or more data communication media such as wires, cables,optical fibers, and the like and/or by one or more wireless datatransfer protocols. In some embodiments, the processing module 106 maybe implemented integrally with the sensor 102.

Generally, the processing of the dual-axis accelerometry signalscomprises at least one of (i) a process in which at least a portion ofthe A-P signal and at least a portion of the S-I signal are analyzedindividually by calculating the meta-features of each signal separatelyfrom the other channel or (ii) a process combining at least a portion ofthe axis-specific vibrational data for the A-P axis with at least aportion of the axis-specific vibrational data for the S-I axis and thenextracting meta-features from the combined data. Then the swallowingevent can be classified based on the extracted meta-features. Inapplying this approach, the swallowing events may be effectivelyclassified as a normal swallowing event or a potentially impairedswallowing events (e.g., unsafe and/or inefficient). Preferably theclassification is automatic such that no user input is needed for thedual-axis accelerometry signals to be processed and used forclassification of the swallow.

FIG. 3 illustrates a non-limiting embodiment of a method 500 forclassifying a swallowing event. At Step 502, dual-axis accelerometrydata for both the S-I axis and the A-P axis is acquired or provided forone or more swallowing events, for example dual-axis accelerometry datafrom the sensor 102.

At Step 504, the dual-axis accelerometry data can optionally beprocessed to condition the accelerometry data and thus facilitatefurther processing thereof. For example, the dual-axis accelerometrydata may be filtered, denoised, and/or processed for signal artifactremoval (“preprocessed data”). In an embodiment, the dual-axisaccelerometry data is subjected to an inverse filter, which may includevarious low-pass, band-pass and/or high-pass filters, followed by signalamplification. A denoising subroutine can then applied to the inversefiltered data, preferably processing signal wavelets and iterating tofind a minimum mean square error.

In an embodiment, the preprocessing may comprise a subroutine for theremoval of movement artifacts from the data, for example, in relation tohead movement by the patient. Additionally or alternatively, othersignal artifacts, such as vocalization and blood flow, may be removedfrom the dual-axis accelerometry data. Nevertheless, the method 500 isnot limited to a specific embodiment of the preprocessing of theaccelerometry data, and the preprocessing may comprise any known methodfor filtering, denoising and/or removing signal artifacts.

At Step 506, the accelerometry data (either raw or preprocessed) canthen be automatically or manually segmented into distinct swallowingevents. Preferably the accelerometry data is automatically segmented. Inan embodiment, the segmentation is automatic and energy-based.Additionally or alternatively, manual segmentation may be applied, forexample by visual inspection of the data. The method 500 is not limitedto a specific process of segmentation, and the process of segmentationcan be any segmentation process known to one skilled in this art.

At Step 508, meta-feature based representation of the accelerometry datacan be performed. For example, one or more time-frequency domainfeatures can be calculated for each axis-specific data set. Combinationsof extracted features may be considered herein without departing fromthe general scope and nature of the present disclosure. Preferablydifferent features are extracted for each axis-specific data set, but insome embodiments the same features may be extracted in each case.Furthermore, other features may be considered for feature extraction,for example, including one or more time, frequency and/or time-frequencydomain features (e.g., mean, variance, center frequency, etc.).

At Step 510 (which is optional), a subset of the meta-features may beselected for classification, for example based on the previous analysisof similar extracted feature sets derived during classifier trainingand/or calibration. For example, in one embodiment, the most prominentfeatures or feature components/levels extracted from the classifiertraining data set are retained as most likely to provide classifiableresults when applied to new test data, and are thus selected to define areduced feature set for training the classifier and ultimately enablingclassification. For instance, in the context of wavelet decompositions,or other such signal decompositions, techniques such as lineardiscriminant analysis, principle component analysis or other suchtechniques effectively implemented to qualify a quantity and/or qualityof information available from a given decomposition level, may be usedon the training data set to preselect feature components or levels mostlikely to provide the highest level of usable information in classifyingnewly acquired signals. Such preselected feature components/levels canthen be used to train the classifier for subsequent classifications.Ultimately, these preselected features can be used in characterizing theclassification criteria for subsequent classifications.

Accordingly, where the device has been configured to operate from areduced feature set, such as described above, this reduced feature setcan be characterized by a predefined feature subset or feature reductioncriteria that resulted from the previous implementation of a featurereduction technique on the classifier training data set. Newly acquireddata can thus proceed through the various pre-processing andsegmentation steps described above (steps 504, 506), the variousswallowing events so identified then processed for feature extraction atstep 508 (e.g., full feature set), and those features corresponding withthe preselected subset retained at step 510 for classification at step512.

While the above exemplary approach contemplates a discrete selection ofthe most prominent features, other techniques may also readily apply.For example, in some embodiments, the results of the feature reductionprocess may rather be manifested in a weighted series or vector forassociation with the extracted feature set in assigning a particularweight or level of significance to each extracted feature component orlevel during the classification process. In particular, selection of themost prominent feature components to be used for classification can beimplemented via linear discriminant analysis (LDA) on the classifiertraining data set. Consequently, feature extraction and reduction can beeffectively used to distinguish safe swallows from potentially unsafeswallows, and efficient swallows from potentially inefficient swallows.In this regard, the extraction of the selected features from new testdata can be compared to preset classification criteria established as afunction of these same selected features as previously extracted andreduced from an adequate training data set, to classify the new testdata as representative of a normal vs. impaired swallow (e.g., safeswallows vs. unsafe swallows, and/or efficient swallows vs. inefficientswallows). As will be appreciated by the skilled artisan, other featuresets such as frequency, time and/or time-frequency domain features maybe used.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with segmentation preprocessing such as swallowsegmentation using a spectrogram. An example of such a meta-feature issegment length.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with non-segmented spectrogram preprocessing, forexample, for analyzing the audio frequency band of the accelerationspectra, the number of detected peaks in sound power measurement,measures of sound direction in respect to the sensor location, number ofswallows based on spectrogram methods, detected noise artefacts on thesignal and/or difference between S-I and A-P signals (residual) (one ormore correlation coefficients between residual and basic signals, orresidual peaking feature). In an embodiment, the accelerometer can alsodetect voices.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with integrating velocity and position from sensorsignals preprocessing, for example with measures like maximum values,standard deviations, between signal difference and intra signaldifference between different segments of signal (for example first thirdin comparison to last third segment of the position/velocity signal) andamount of change in the velocity or position in comparison to theinitial movement or location at the beginning of the measurement;searching number of signal peaks on the S-I or A-P velocity measures orfrom combined velocity measures of both signals; or regression line andzero-crossings of the residual between S-I and A-P signals.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with segmented spectrogram preprocessing, for examplesignal basic statistics from spectrogram (e.g., variance or standarddeviation calculated over spectrogram for different bandwidths, whichcan be calculated separately for each of S-I or A-P signal channel, oras common for both; spectral entropy at different bandwidths; directionof focus of spectrogram components; spectral entropy for the spectrogramtaken as a difference between low-frequencies and high-frequencies; orprincipal component model (PCA) from spectrogram, measuring percentageof variance explained by 1st or 2nd PCA component, either in time orfrequency axis.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with specture: Texture features of intensity imageformed from visualization of spectrogram.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with segmented head and swallow signals preprocessing,such as signal entropy.

In a preferred embodiment, one or more of the extracted meta-featurescan be Mean Absolute Value of the acceleration signals within swallowsegment, number of detected swallows, Waveform Length of the swallowsegment, distance between swallow and detected vocal/speech, number ofdetected coughs.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with head signal, which is computed from the AP and SIsignal low-frequency trends for tracking of the head motion.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with swallow signal, which is computed from the AP andSI signal mid frequency range for tracking of the swallow motion.

In a preferred embodiment, one or more of the extracted meta-featurescan be associated with cough methods, e.g. measuring average or maximumsignal energy within the detected cough period.

At Step 512, feature classification can be implemented. Extractedfeatures (or a reduced/weighted subset thereof) of acquiredswallow-specific data can be compared with preset classificationcriteria to classify each data set as representative of a normalswallowing event or a potentially impaired swallowing event.

In an embodiment, the method 500 can optionally comprise atraining/validation subroutine Step 516 in which a data setrepresentative of multiple swallows is processed such that eachswallow-specific data set ultimately experiences the preprocessing,feature extraction and feature reduction disclosed herein. A validationloop can be applied to the discriminant analysis-based classifier usinga cross-validation test. After all events have been classified andvalidated, output criteria may be generated for future classificationwithout necessarily applying further validation to the classificationcriteria. Alternatively, routine validation may be implemented to eitherrefine the statistical significance of classification criteria, or againas a measure to accommodate specific equipment and/or protocol changes(e.g. recalibration of specific equipment, for example, upon replacingthe accelerometer with same or different accelerometer type/model,changing operating conditions, new processing modules such as furtherpreprocessing subroutines, artifact removal, additional featureextraction/reduction, etc.).

The classification can be used to determine and output which swallowingevent represented a normal swallowing event as compared to apenetration, an aspiration, a swallowing safety impairment and/or answallowing efficiency impairment at Step 514. In some embodiments, theswallowing event can be further classified as a safe event or an unsafeevent.

For example, the processing module 106 and/or a device associated withthe processing module 106 can comprise a display that identifies aswallow or an aspiration using images such as text, icons, colors,lights turned on and off, and the like. Alternatively or additionally,the processing module 106 and/or a device associated with the processingmodule 106 can comprise a speaker that identifies a swallow or anaspiration using auditory signals. The present disclosure is not limitedto a specific embodiment of the output, and the output can be any meansby which the classification of the swallowing event is identified to auser of the device 100, such as a clinician or a patient.

The output may then be utilized in screening/diagnosing the testedcandidate and providing appropriate treatment, further testing, and/orproposed dietary or other related restrictions thereto until furtherassessment and/or treatment may be applied. For example, adjustments tofeedings can be based on changing consistency or type of food and/or thesize and/or frequency of mouthfuls being offered to the patient.

Alternative types of vibration sensors other than accelerometers can beused with appropriate modifications to be the sensor 102. For example, asensor can measure displacement (e.g, a microphone), while theprocessing module 106 records displacement signals over time. As anotherexample, a sensor can measure velocity, while the processing module 106records velocity signals over time. Such signals can then be convertedinto acceleration signals and processed as disclosed herein and/or byother techniques of feature extraction and classification appropriatefor the type of received signal.

Another aspect of the present disclosure is a method of treatingdysphagia. The term “treat” includes both prophylactic or preventivetreatment (that prevent and/or slow the development of dysphagia) andcurative, therapeutic or disease-modifying treatment, includingtherapeutic measures that cure, slow down, lessen symptoms of, and/orhalt progression of dysphagia; and treatment of patients at risk ofdysphagia, for example patients having another disease or medicalcondition that increase their risk of dysphagia relative to a healthyindividual of similar characteristics (age, gender, geographic location,and the like). The term does not necessarily imply that a subject istreated until total recovery. The term “treat” also refers to themaintenance and/or promotion of health in an individual not sufferingfrom dysphagia but who may be susceptible to the development ofdysphagia. The term “treat” also includes the potentiation or otherwiseenhancement of one or more primary prophylactic or therapeutic measures.The term “treat” further includes the dietary management of dysphagia orthe dietary management for prophylaxis or prevention of dysphagia. Atreatment can be conducted by a patient, a clinician and/or any otherindividual or entity.

The method of treating dysphagia comprises using any embodiment of thedevice 100 disclosed herein and/or performing any embodiment of themethod 500 disclosed herein. The method can further comprise adjusting afeeding administered to the patient based on the classification, forexample by changing a consistency of the feeding, changing a type offood in the feeding, changing a size of a portion of the feedingadministered to the patient, changing a frequency at which portions ofthe feeding are administered to the patient, or combinations thereof.

In an embodiment, the method prevents aspiration pneumonia fromdysphagia. In an embodiment, the dysphagia is oral pharyngeal dysphagiaassociated with a condition selected from the group consisting ofcancer, cancer chemotherapy, cancer radiotherapy, surgery for oralcancer, surgery for throat cancer, a stroke, a brain injury, aprogressive neuromuscular disease, neurodegenerative diseases, anelderly age of the patient, and combinations thereof. As used herein, an“elderly” human is a person with a chronological age of 65 years orolder.

It should be understood that various changes and modifications to thepresently preferred embodiments described herein will be apparent tothose skilled in the art. Such changes and modifications can be madewithout departing from the spirit and scope of the present subjectmatter and without diminishing its intended advantages. It is thereforeintended that such changes and modifications be covered by the appendedclaims.

1. A device for identifying a possible swallowing impairment in acandidate during execution of a swallowing event, the device comprising:an accelerometer configured to acquire axis-specific vibrational dataalong an anterior-posterior (AP) axis and a superior-inferior (SI) axisof the candidate's throat, the axis-specific vibrational data isrepresentative of the swallowing event; a processing module that is alocal or remote computing device operatively coupled to theaccelerometer, the processing module configured for processing theaxis-specific data to extract meta-features from the data, one or moreof the meta-features associated with an approach selected from the groupconsisting of (i) swallow segmentation using spectrogram, (ii) sounddirection for a non-segmented spectrogram, (iii) difference between SIand AP signals regarding correlation coefficient between residual andbasic signals for a non-segmented spectrogram, (iv) difference betweenSI and AP signals regarding residual peaking feature for a non-segmentedspectrogram, (v) velocity and position for integrating velocity andposition from sensor signals, (vi) basic signal statistics for segmentedspectrogram, (vii) spectral entropy at different bandwidths forsegmented spectrogram, (viii) direction of focus of spectrogramcomponents for segmented spectrogram, (ix) spectral entropy for thespectrogram taken as a difference between low-frequencies andhigh-frequencies for segmented spectrogram, (x) PCA from spectrogram,measuring percentage of variance explained by 1st or 2nd PCA component,either in time or frequency axis for segmented spectrogram, (xi) texturefeatures from spectrogram image for specture, and (xii) signal entropyfor head and swallow signals, and the processing module configured toclassify the swallowing event as one of a plurality of classificationsbased on the meta-features extracted from the vibrational data, theplurality of classifications comprising a first classificationindicative of normal swallowing and a second classification indicativeof possibly impaired swallowing.
 2. The device of claim 1, wherein theprocessing module is configured to automatically extract themeta-features from the data.
 3. The device of claim 1, wherein theprocessing module is configured to automatically use the meta-featuresextracted from the data to classify the swallowing event.
 4. The deviceof claim 1, wherein the processing module is configured to compare themeta-features extracted from the data to preset classification criteriato classify the swallowing event. 5-6. (canceled)
 7. The device of claim1, wherein the second classification is indicative of at least one of aswallowing safety impairment or a swallowing efficiency impairment. 8.The device of claim 1, wherein the second classification is indicativeof at least one of penetration or aspiration, and the processing moduleis configured to further classify the swallowing event as a first eventindicative of a safe event or a second event indicative of an unsafeevent.
 9. The device of claim 1, wherein the processing module isconfigured to classify multiple successive swallowing events byclassifying the data for each of the successive swallowing events asindicative of one of the first classification or the secondclassification.
 10. The device of claim 1, wherein the processing moduledisplays the classification.
 11. A method for classifying cervicalaccelerometry data acquired for a swallowing event to identify apossible swallowing impairment in a candidate, the method comprising:receiving axis-specific vibrational data for an anterior-posterior (AP)axis and a superior-inferior (SI) axis and representative of theswallowing event, a processing module that is a local or remotecomputing device operatively coupled to an accelerometer receives theaxis-specific vibrational data from the accelerometer; processing theaxis-specific data to extract meta-features from the data, one or moreof the meta-features associated with an approach selected from the groupconsisting of (i) swallow segmentation using spectrogram, (ii) sounddirection for a non-segmented spectrogram, (iii) difference between SIand AP signals regarding correlation coefficient between residual andbasic signals for a non-segmented spectrogram, (iv) difference betweenSI and AP signals regarding residual peaking feature for a non-segmentedspectrogram, (v) velocity and position for integrating velocity andposition from sensor signals, (vi) basic signal statistics for segmentedspectrogram, (vii) spectral entropy at different bandwidths forsegmented spectrogram, (viii) direction of focus of spectrogramcomponents for segmented spectrogram, (ix) spectral entropy for thespectrogram taken as a difference between low-frequencies andhigh-frequencies for segmented spectrogram, (x) PCA from spectrogram,measuring percentage of variance explained by 1st or 2nd PCA component,either in time or frequency axis for segmented spectrogram, (xi) texturefeatures from spectrogram image for specture, and (xii) signal entropyfor head and swallow signals; and outputting a classification of theswallowing event as one of a plurality of classifications based on themeta-features extracted from the data, the plurality of classificationscomprising a first classification indicative of normal swallowing and asecond classification indicative of possibly impaired swallowing, andthe processing module outputs the classification.
 12. The method ofclaim 11, wherein the processing module automatically extracts themeta-features from the data.
 13. The method of claim 11, wherein theprocessing module automatically uses the meta-features extracted fromthe data to classify the swallowing event.
 14. The method of claim 11,comprising comparing the meta-features extracted from the data to presetclassification criteria to classify the swallowing event on theprocessing module.
 15. The method of claim 14, wherein the presetclassification criteria are defined for each of swallowing safety andswallowing efficiency.
 16. The method of claim 15, wherein the presetclassification criteria are defined by features previously extracted andclassified from a known training data set.
 17. The method of claim 11,wherein the second classification is indicative of at least one of aswallowing safety impairment or a swallowing efficiency impairment. 18.The method of claim 11, wherein the second classification is indicativeof at least one of penetration or aspiration, and the method comprisesfurther classifying the swallowing event as a first event indicative ofa safe event or a second event indicative of an unsafe event.
 19. Themethod of claim 11, comprising classifying successive swallowing eventsby classifying the data for each of the successive swallowing events asindicative of one of the first classification or the secondclassification.
 20. The method of claim 11, comprising displaying theclassification on the processing device.
 21. A method of treatingdysphagia in a patient, the method comprising: positioning a sensorexternally on the throat of the patient, the sensor acquiringvibrational data representing swallowing activity and associated with ananterior-posterior axis and a superior-inferior axis of the throat, thesensor operatively connected to a processing module configured toprocess the axis-specific data to extract meta-features from the data,one or more of the meta-features associated with an approach selectedfrom the group consisting of (i) swallow segmentation using spectrogram,(ii) sound direction for a non-segmented spectrogram, (iii) differencebetween SI and AP signals regarding correlation coefficient betweenresidual and basic signals for a non-segmented spectrogram, (iv)difference between SI and AP signals regarding residual peaking featurefor a non-segmented spectrogram, (v) velocity and position forintegrating velocity and position from sensor signals, (vi) basic signalstatistics for segmented spectrogram, (vii) spectral entropy atdifferent bandwidths for segmented spectrogram, (viii) direction offocus of spectrogram components for segmented spectrogram, (ix) spectralentropy for the spectrogram taken as a difference betweenlow-frequencies and high-frequencies for segmented spectrogram, (x) PCAfrom spectrogram, measuring percentage of variance explained by 1st or2nd PCA component, either in time or frequency axis for segmentedspectrogram, (xi) texture features from spectrogram image for specture,and (xii) signal entropy for head and swallow signals, the processingmodule configured to classify the swallowing event as one of a pluralityof classifications based on the meta-features extracted from thevibrational data, the plurality of classifications comprising a firstclassification indicative of normal swallowing and a secondclassification indicative of possibly impaired swallowing; and adjustinga feeding administered to the patient based on the classification. 22.The method of claim 21 wherein the adjusting of the feeding is selectedfrom the group consisting of: changing a consistency of the feeding,changing a type of food in the feeding, changing a size of a portion ofthe feeding administered to the patient, changing a frequency at whichportions of the feeding are administered to the patient, andcombinations thereof.